Abstract

We develop a discrete approach in the theory of randomized machine learning that is aimed at application to nonlinear models. We formulate the problem of entropy estimation of probability distributions and measurement noise for discrete nonlinear models. Issues related to the application of such models to forecasting problems, in particular, the problem of generating entropy-optimal distributions, are considered. The proposed methods are demonstrated on the solution of the problem of forecasting the total number of persons infected with novel coronavirus SARS-CoV-2 in Germany in 2020.

中文翻译：

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非线性模型的随机机器学习在预测流行病过程中的应用

摘要

我们在随机机器学习理论中开发了一种离散方法，旨在应用于非线性模型。我们制定了离散非线性模型的概率分布和测量噪声的熵估计问题。考虑了与将此类模型应用于预测问题相关的问题，特别是生成熵最优分布的问题。所提出的方法在预测2020年德国新型冠状病毒SARS-CoV-2感染总人数的问题的解决方案中得到了证明。